Enhancing the heart failure survival prediction by using artificial intelligence

Heart failure is a widespread cardiovascular ailment posing a significant threat to global health with an estimated 17.9 million annual fatalities. This study focuses on 299 patients with advanced heart failure (classified as III/IV) and left ventricular systolic dysfunction. Our examination involves assessing the concordance index for model evaluation. To augment our predictive capacities, we proposed a DS-NN. This model was compared against the random survival forest, gradient boosting, gradient boosting least square and the Cox proportional hazard model. Notably, DS-NN showcased superior prowess compared to the other five models with concordance index values of 0.73 and 0.72 for the training and testing sets, respectively. This implies that incorporating deep learning into survival prediction holds promise for more accuracy and offering clinicians' valuable insights for treatment decisions. This ultimately leads to improved survival outcomes and the avoidance of unnecessary interventions.